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Machine Learning-based Prognostic Models for Improved Asthma Management Using UK-Wide Electronic Health Records


College of Medicine and Veterinary Medicine

Edinburgh United Kingdom Health Informatics

About the Project

Background/rationale

Asthma exacerbations can affect people with asthma of any age, ethnicity, and severity. In the UK, there are at least 6.3 million primary care consultations, 93000 hospital inpatient episodes and 1400 deaths attributed to asthma costing the UK public sector over £1.1 billion (Mukherjee et al., 2016). Most people with asthma have long periods where they are either asymptomatic or experience relatively mild symptoms. Occasionally, however, people with asthma experience exacerbations which can potentially be life-threatening. Asthma self-management plans aim to support patients/HCPs in identifying when asthma control is deteriorating and then encouraging patients to modify their treatment accordingly to improve asthma control. At present, this is largely a qualitative process and there is no widely used algorithm to help predict the risk of asthma exacerbations. Most previous related work have identified one or more risk factors associated with asthma exacerbations. However, these studies typically assess each risk factor independently and do not assess the predictive performance of an optimal combination of factors in individual patients.

Aims

The AUKCAR has an existing agreement with Optimum Patient Care providing access to one of the largest UK-wide primary care databases, Optimum Patient Care Research Database (OPCRD). This database, to date, has over 10 million patients from across the UK with fully characterised chronic disease data. This project aims to leverage such large electronic health records to validate existing and develop new asthma exacerbation risk prediction models with machine learning techniques (Hussain et al., 2020).

 

Supervisors

·        Dr Syed Ahmar Shah, Usher Institute, Edinburgh Medical School, The University of Edinburgh

·        Professor Aziz Sheikh, Usher Institute, Edinburgh Medical School, The University of Edinburgh

In collaboration with:

·        Professor Andrew Wilson, University of East Anglia

 

Requirements

In this project, we seek to appoint someone with a quantitative background who is keen to apply those quantitative techniques in healthcare. A strong academic track record with a 2:1 or higher in a relevant undergraduate degree or its equivalent if outside the UK. It is also desirable to have a strong performance in a relevant postgraduate degree. Demonstrable experience in one or more of the following is desirable: machine learning, previous research experience with healthcare datasets or electronic health, programming in MATLAB/Python/R etc. Candidates with engineering, mathematics or computer science degrees are particularly encouraged to apply. The successful candidate will work in a highly interdisciplinary environment and should be able to work independently and as part of a distributed international team.

 

Following interview, the selected candidate will need to apply and be accepted for a place on the University of Edinburgh PhD programme. Details about the PhD in Medical Informatics programme can be found here:

https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=924

Application procedure

This position is available for UK/EU applicants only.

Please provide a CV, a personal statement detailing your research interests and reasons for applying, degree certificate(s), marks for your degree(s) and 2 written academic references. All documents should be in electronic format and sent via e-mail to:

The closing date for applications is: 1 May 2021

Interviews will be held during May 2021.

 

Further information

The Asthma UK Centre for Applied Research (AUKCAR) is a virtual centre comprising a network of Universities across the UK, led by the University of Edinburgh and Queen Mary University of London. Our vision, aligned with that of Asthma UK, is a world where asthma attacks do not happen. Our research focuses on developing, testing and implementing interventions which have the potential to achieve substantial, sustained reductions in asthma morbidity and mortality.

 

The AUKCAR Postgraduate Training Programme aims to enrol the most exceptional candidates and nurture them to become the next generation of leaders in world-class applied asthma research. A key feature of these studentships is the opportunity to be part of a UK-wide Centre offering unsurpassed networking opportunities with leading UK applied asthma researchers. Students will be based in a ‘home’ University (in this case Edinburgh) but will have at least one supervisor from an AUKCAR partner institution.

The prospective student will join the DIME group (https://dimeresearch.com/) based in the Usher Institute at the University of Edinburgh and will be part of the AUKCAR network (https://www.aukcar.ac.uk/). Please contact Dr Syed Ahmar Shah at for further queries.


Funding Notes

This studentship is funded by the Scottish Government’s Chief Scientist Office. Funding includes Tuition Fees at UK/EU rates (£4,407 per annum in 20/21) for 3 years and an annual stipend at the RCUK rate of £15,285 (2020/21) per annum as well as Research Costs (including conference attendance) of £10,000 per annum for 3 years.
This studentship covers UK/EU tuition fees only (any eligible non-EU candidates must fund the remainder of the overseas tuition fee).

References

Hussain, Z. et al. (2020) ‘Predicting the risk of asthma attacks in children, adolescents and adults: protocol for a machine learning algorithm derived from a primary care-based retrospective cohort’, BMJ open. British Medical Journal Publishing Group, 10(7), p. e036099.
Mukherjee, M. et al. (2016) ‘The epidemiology, healthcare and societal burden and costs of asthma in the UK and its member nations: Analyses of standalone and linked national databases’, BMC Medicine. BioMed Central Ltd., 14(1). doi: 10.1186/s12916-016-0657-8.

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